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## Calculate 2x2 Matrix Inverse (medium)

#### Example

## Calculating the Inverse of a 2x2 Matrix

The inverse of a matrix \(A\) is another matrix, often denoted \(A^{-1}\), such that:
\[
AA^{-1} = A^{-1}A = I
\]
where \(I\) is the identity matrix. For a 2x2 matrix:
\[
A = \begin{pmatrix}
a & b \\
c & d
\end{pmatrix}
\]
The inverse is:
\[
A^{-1} = \frac{1}{\det(A)} \begin{pmatrix}
d & -b \\
-c & a
\end{pmatrix}
\]
provided that the determinant \(\det(A) = ad - bc\) is non-zero. If \(\det(A) = 0\), the matrix does not have an inverse.
This process is critical in many applications including solving systems of linear equations, where the inverse is used to find solutions efficiently.

Write a Python function that calculates the inverse of a 2x2 matrix. Return 'None' if the matrix is not invertible.

Example: input: matrix = [[4, 7], [2, 6]] output: [[0.6, -0.7], [-0.2, 0.4]] reasoning: The inverse of a 2x2 matrix [a, b], [c, d] is given by (1/(ad-bc)) * [d, -b], [-c, a], provided ad-bc is not zero.

def inverse_2x2(matrix: list[list[float]]) -> list[list[float]]: a, b, c, d = matrix[0][0], matrix[0][1], matrix[1][0], matrix[1][1] determinant = a * d - b * c if determinant == 0: return None inverse = [[d/determinant, -b/determinant], [-c/determinant, a/determinant]] return inverse

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